Identifying Interesting Visitors Through Transductive Support Vector Machine Web Log Classifier


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Abstract


Log file data can provide precious insight into web usage mining. Web access log analysis is to analyze the patterns of web site usage and the features of user behavior. Visitors’ characteristics of a web site are analyzed after the sessions are constructed using web log. This paper simplifies the method of classifying interesting users from a given set of web logs of an e-commerce web server. Users are classified into two categories; (1). Users who are really interested in the buying the product, (2). Users who simply browse the site just to get familiarize about the site. Transductive Support Vector Machine (TSVM) algorithm is used to classify the web logs into these two categories of users
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Keywords


Web Usage Mining; Session Construction; SVM (Support Vector Machine); Transductive SVM (TSVM); Web Log Classification

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References


Jie Zhang, Ali., A. Ghorbani, “The Reconstruction of User Sessions from a Server Log Using Improved Time-oriented Heuristics”, Proceedings of the Second Annual Conference on Communication Networks and Services, IEEE, May 2004, pp. 315-322.

Alka Gangrade , Durgesh Kumar Mishra and Ravindra Patel ,“Classification Rule Mining through SMCfor Preserving Privacy Data Mining: A Review”, International Conference on Machine Learning and Computing IPCSIT, Singapore, 2009, vol.3 (2011),IACSIT Press, pp. 431 to 434.

Mahesh Thylore Ramakrishna, Latha Kolal Gowdar,Malatesh Somashekar Havanur and Banur Puttappa Mallikarjuna Swamy, “Web Mining: Key Accomplishments, Applications and Future Directions”, International Conference on Data Storage and Data Engineering, February 2010.

Hanady Abdulsalam, David B. Skillicorn, and Patrick Martin, “Classification Using Streaming Random Forests”,IEEE Transactions on Knowledge And Data Engineering, January 2011, Vol. 23, No.1., pp.22-36.

Hidenao Abe , “Development of a Classification Rule Mining Framework by Using Temporal Pattern Extraction”, New fundamental technologies in datamining, January 2011, pp. 493-504.

Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho,and Sau Dan Lee, “Decision Trees for Uncertain Data”, IEEE Transactions On Knowledge And Data Engineering, January 2011, Vol. 23, No. 1, pp 63-78.

Veronica S. Moertini, “Towards The Use Of C4.5 Algorithm For Classifying Banking Dataset”,Integral,, 2003, Vol. 8 No. 2.

Salvatore Ruggieri, “Efficient C4.5", IEEE Transactions on Knowledge and Data Engineering, March/April 2002, Vol. 14, No. 2, pp. 434 -444.

Thales Sehn Korting, “C4.5 algorithm and Multivariate Decision Trees”, Image Processing Division, National Institute for Space Research – INPE Sao Jose dos Campos– SP, Brazil, 2006.

Mahdi Khosravi, Mohammad and J. Tarokh,“Dynamic Mining of Users Interest Navigation PatternsUsing Naive Bayesian Method”, IEEE 6th International Conference on Intelligent Computer Communication and Processing Transaction, 2010, pp. 119-122

Abdelhamid, D., Chaouki, B.M., Abdelmalik, T.A., An SVM based system for automatic dates sorting, (2010) International Review on Computers and Software (IRECOS), 5 (4), pp. 423-428.

A.K. Santra, S. Jayasudha, “Classification of Web Log Data to Identify Interested Users Using Naïve Bayesian Classification”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012 ISSN (Online): 1694-0814

Jeffrey Xu Yu, Yuming Ou, Chengqi Zhang and Shichao Zhang, “Identifying Interesting Visitors through Web Log Classification”, IEEE Intelligent System, 1541-1672/05, May/June 2005

Amini, M., and Gallinari, P. (2003). Semi-supervised learning with an explicit label-error model for misclassified data. IJCAI2003.

Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.

Joachims, T. (1999). Transductive inference fortext classification using support vector machines.ICML1999.


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